GPS／INS组合导航的变分贝叶斯自适应卡尔曼滤波

Variational bayesian adaptive Kalman filtering for GPS／INS integrated navigation

摘要:

为解决GPS／INS组合导航的数据融合问题中卡尔曼滤波器因噪声统计特性会发生变化而性能严重退化的问题,针对组合导航的系统模型提出并推导了一种基于变分贝叶斯学习的自适应卡尔曼滤波算法．该方法从概率角度将系统状态与噪声的统计矩一起作为待估计的随机变量,在每次递推地对状态进行估计之前,用变分贝叶斯学习迭代逼近得到噪声的后验分布．仿真结果证明: 在组合导航系统中,该自适应算法能够较好地跟踪变化的噪声方差,并对速度、位置等系统状态进行估计．

Abstract:

To circumvent the problem in GPS／INS integrated navigation for data infusion that Kalman filter degrades severely since the statistics of the noise might be time-variant, an adaptive Kalman filtering algorithm based on variational Bayesian learning is suggested and used in the integrated navigation system model in which both the moment of noise and the states are considered as stochastic parameters and estimated together. Using a probabilistic approach, a concrete derivation is given to represent how variational Bayesian learning works in a recursive way to approximate the true posterior of the noise together with the states. Experimental results demonstrate that the proposed filter is adaptive and performs well in tracking variances of the noise and estimating the states including position and velocity in GPS／INS integrated navigation system.